import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F

x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[0.0], [0.0], [1.0]])

class LogisticRegression(torch.nn.Module):
    def __init__(self):
        super(LogisticRegression, self).__init__()
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = F.sigmoid(self.linear(x))#此处的sigmoid是一个函数，来自torch.nn.functional
        return y_pred


model = LogisticRegression()

criterion = torch.nn.BCELoss(size_average = False)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

x = np.linspace(0, 10, 200)
x_test = torch.Tensor(x).view(200, 1)
y_test = model(x_test)
y = y_test.data.numpy()
plt.plot(x, y)
plt.plot([0, 10], [0.5, 0.5], color = 'r')
plt.xlabel('Hours')
plt.ylabel('Probability of Pass')
plt.grid()
plt.show()
